Histopathology Research Template 🔬
Describe Materials and Methods as highlighted in (Knijn, Simmer, and Nagtegaal 2015).2
Describe patient characteristics, and inclusion and exclusion criteria
Describe treatment details
Describe the type of material used
Specify how expression of the biomarker was assessed
Describe the number of independent (blinded) scorers and how they scored
State the method of case selection, study design, origin of the cases, and time frame
Describe the end of the follow-up period and median follow-up time
Define all clinical endpoints examined
Specify all applied statistical methods
Describe how interactions with other clinical/pathological factors were analyzed
Codes for general settings.3
Setup global chunk settings4
knitr::opts_chunk$set(
eval = TRUE,
echo = TRUE,
fig.path = here::here("figs/"),
message = FALSE,
warning = FALSE,
error = FALSE,
cache = FALSE,
comment = NA,
tidy = TRUE,
fig.width = 6,
fig.height = 4
)Load Library
see R/loadLibrary.R for the libraries loaded.
Codes for generating fake data.5
Generate Fake Data
This code generates a fake histopathological data. Some sources for fake data generation here6 , here7 , here8 , here9 , here10 , here11 , here12 , here13 , and here14 .
Use this code to generate fake clinicopathologic data
Codes for importing data.15
Read the data
library(readxl)
mydata <- readxl::read_excel(here::here("data", "mydata.xlsx"))
# View(mydata) # Use to view data after importingAdd code for import multiple data purrr reduce
Codes for reporting general features.16
Dataframe Report
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others
- Name: 249 entries: Adayah, n = 1; Adeja, n = 1; Adelaina, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Male, n = 135; Female, n = 114 (1 missing)
- Age: Mean = 49.05, SD = 13.68, range = [25, 73], 1 missing
- Race: 6 entries: White, n = 158; Hispanic, n = 46; Black, n = 33 and 3 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 192; Present, n = 57 (1 missing)
- LVI: 2 entries: Absent, n = 163; Present, n = 86 (1 missing)
- PNI: 2 entries: Absent, n = 174; Present, n = 75 (1 missing)
- Death: 2 levels: FALSE (n = 81); TRUE (n = 168) and missing (n = 1)
- Group: 2 entries: Treatment, n = 128; Control, n = 121 (1 missing)
- Grade: 3 entries: 3, n = 101; 1, n = 80; 2, n = 68 (1 missing)
- TStage: 4 entries: 4, n = 102; 3, n = 73; 2, n = 52 and 1 other (1 missing)
- Anti-X-intensity: Mean = 2.41, SD = 0.62, range = [1, 3], 1 missing
- Anti-Y-intensity: Mean = 1.97, SD = 0.77, range = [1, 3], 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 143; Present, n = 106 (1 missing)
- Valid: 2 levels: FALSE (n = 139); TRUE (n = 110) and missing (n = 1)
- Smoker: 2 levels: FALSE (n = 125); TRUE (n = 124) and missing (n = 1)
- Grade_Level: 3 entries: high, n = 96; moderate, n = 79; low, n = 74 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101
250 observations with 21 variables
19 variables containing missings (NA)
0 variables with no variance
Codes for defining variable types.19
print column names as vector
c("ID", "Name", "Sex", "Age", "Race", "PreinvasiveComponent",
"LVI", "PNI", "LastFollowUpDate", "Death", "Group", "Grade",
"TStage", "Anti-X-intensity", "Anti-Y-intensity", "LymphNodeMetastasis",
"Valid", "Smoker", "Grade_Level", "SurgeryDate", "DeathTime")
See the code as function in R/find_key.R.
keycolumns <- mydata %>% sapply(., FUN = dataMaid::isKey) %>% as_tibble() %>% select(which(.[1,
] == TRUE)) %>% names()
keycolumns[1] "ID" "Name"
Get variable types
# A tibble: 4 x 4
type cnt pcnt col_name
<chr> <int> <dbl> <list>
1 character 11 57.9 <chr [11]>
2 logical 3 15.8 <chr [3]>
3 numeric 3 15.8 <chr [3]>
4 POSIXct POSIXt 2 10.5 <chr [2]>
mydata %>% select(-keycolumns, -contains("Date")) %>% describer::describe() %>% knitr::kable(format = "markdown")| .column_name | .column_class | .column_type | .count_elements | .mean_value | .sd_value | .q0_value | .q25_value | .q50_value | .q75_value | .q100_value |
|---|---|---|---|---|---|---|---|---|---|---|
| Sex | character | character | 250 | NA | NA | Female | NA | NA | NA | Male |
| Age | numeric | double | 250 | 49.048193 | 13.6814894 | 25 | 37 | 48 | 61 | 73 |
| Race | character | character | 250 | NA | NA | Asian | NA | NA | NA | White |
| PreinvasiveComponent | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| LVI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| PNI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| Death | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Group | character | character | 250 | NA | NA | Control | NA | NA | NA | Treatment |
| Grade | character | character | 250 | NA | NA | 1 | NA | NA | NA | 3 |
| TStage | character | character | 250 | NA | NA | 1 | NA | NA | NA | 4 |
| Anti-X-intensity | numeric | double | 250 | 2.405623 | 0.6222751 | 1 | 2 | 2 | 3 | 3 |
| Anti-Y-intensity | numeric | double | 250 | 1.967871 | 0.7718405 | 1 | 1 | 2 | 3 | 3 |
| LymphNodeMetastasis | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| Valid | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Smoker | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Grade_Level | character | character | 250 | NA | NA | high | NA | NA | NA | moderate |
| DeathTime | character | character | 250 | NA | NA | MoreThan1Year | NA | NA | NA | Within1Year |
Plot variable types
# https://github.com/ropensci/visdat
# http://visdat.njtierney.com/articles/using_visdat.html
# https://cran.r-project.org/web/packages/visdat/index.html
# http://visdat.njtierney.com/
# visdat::vis_guess(mydata)
visdat::vis_dat(mydata)character variablescharacterVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "character") %>% dplyr::select(col_name) %>% pull() %>%
unlist()
characterVariables [1] "Sex" "Race" "PreinvasiveComponent"
[4] "LVI" "PNI" "Group"
[7] "Grade" "TStage" "LymphNodeMetastasis"
[10] "Grade_Level" "DeathTime"
categorical variablescategoricalVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"factor") %>% dplyr::select(column_name) %>% dplyr::pull()
categoricalVariablescharacter(0)
continious variablescontiniousVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"numeric" | column_type == "double") %>% dplyr::select(column_name) %>% dplyr::pull()
continiousVariables[1] "Age" "Anti-X-intensity" "Anti-Y-intensity"
numeric variablesnumericVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "numeric") %>% dplyr::select(col_name) %>% pull() %>% unlist()
numericVariables[1] "Age" "Anti-X-intensity" "Anti-Y-intensity"
integer variablesintegerVariables <- mydata %>% select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "integer") %>% dplyr::select(col_name) %>% pull() %>% unlist()
integerVariablesNULL
Codes for overviewing the data.20
reactable::reactable(data = mydata, sortable = TRUE, resizable = TRUE, filterable = TRUE,
searchable = TRUE, pagination = TRUE, paginationType = "numbers", showPageSizeOptions = TRUE,
highlight = TRUE, striped = TRUE, outlined = TRUE, compact = TRUE, wrap = FALSE,
showSortIcon = TRUE, showSortable = TRUE)Summary of Data via summarytools 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
summarytools::view(x = summarytools::dfSummary(mydata %>% select(-keycolumns)), file = here::here("out",
"mydata_summary.html"))Summary via dataMaid 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
dataMaid::makeDataReport(data = mydata, file = here::here("out", "dataMaid_mydata.Rmd"),
replace = TRUE, openResult = FALSE, render = FALSE, quiet = TRUE)Summary via explore 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
mydata %>% select(-dateVariables) %>% explore::report(output_file = "mydata_report.html",
output_dir = here::here("out"))Glimpse of Data
Observations: 250
Variables: 17
$ Sex <chr> "Male", "Female", "Female", "Male", "Male", "Mal…
$ Age <dbl> 29, 47, 56, 67, 68, 69, 69, 63, 54, 41, 48, 31, …
$ Race <chr> "White", "White", "White", "White", "White", "Hi…
$ PreinvasiveComponent <chr> "Absent", "Absent", "Absent", "Present", "Absent…
$ LVI <chr> "Present", "Absent", "Absent", "Present", "Prese…
$ PNI <chr> "Present", "Absent", "Absent", "Absent", "Absent…
$ Death <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE…
$ Group <chr> "Treatment", "Treatment", "Treatment", "Treatmen…
$ Grade <chr> "2", "1", "3", "1", "3", "2", "2", "3", "2", "3"…
$ TStage <chr> "4", "2", "1", "4", "4", "4", "1", "3", "4", "1"…
$ `Anti-X-intensity` <dbl> 3, 2, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 1, 2, 3, 3, …
$ `Anti-Y-intensity` <dbl> 3, 3, 1, 3, 2, 2, NA, 1, 1, 1, 2, 1, 2, 3, 3, 2,…
$ LymphNodeMetastasis <chr> "Present", "Present", "Present", "Present", "Pre…
$ Valid <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, F…
$ Smoker <lgl> TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALS…
$ Grade_Level <chr> "moderate", "high", "low", "moderate", "high", "…
$ DeathTime <chr> "Within1Year", "Within1Year", "Within1Year", "Wi…
variable type na na_pct unique min mean max
1 ID chr 0 0.0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 49.05 73
5 Race chr 1 0.4 7 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 1 0.4 3 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.67 1
11 Group chr 1 0.4 3 NA NA NA
12 Grade chr 1 0.4 4 NA NA NA
13 TStage chr 1 0.4 5 NA NA NA
14 Anti-X-intensity dbl 1 0.4 4 1 2.41 3
15 Anti-Y-intensity dbl 1 0.4 4 1 1.97 3
16 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
17 Valid lgl 1 0.4 3 0 0.44 1
18 Smoker lgl 1 0.4 3 0 0.50 1
19 Grade_Level chr 1 0.4 4 NA NA NA
20 SurgeryDate dat 1 0.4 231 NA NA NA
21 DeathTime chr 0 0.0 2 NA NA NA
Explore
Control Data if matching expectations
visdat::vis_expect(data = mydata, expectation = ~.x == -1, show_perc = TRUE)
visdat::vis_expect(mydata, ~.x >= 25)See missing values
$variables
Variable q qNA pNA qZero pZero qBlank pBlank qInf pInf
1 Valid 250 1 0.4% 139 55.6% 0 - 0 -
2 Smoker 250 1 0.4% 125 50% 0 - 0 -
3 Death 250 1 0.4% 81 32.4% 0 - 0 -
4 Sex 250 1 0.4% 0 - 0 - 0 -
5 PreinvasiveComponent 250 1 0.4% 0 - 0 - 0 -
6 LVI 250 1 0.4% 0 - 0 - 0 -
7 PNI 250 1 0.4% 0 - 0 - 0 -
8 Group 250 1 0.4% 0 - 0 - 0 -
9 LymphNodeMetastasis 250 1 0.4% 0 - 0 - 0 -
10 Grade 250 1 0.4% 0 - 0 - 0 -
11 Anti-X-intensity 250 1 0.4% 0 - 0 - 0 -
12 Anti-Y-intensity 250 1 0.4% 0 - 0 - 0 -
13 Grade_Level 250 1 0.4% 0 - 0 - 0 -
14 TStage 250 1 0.4% 0 - 0 - 0 -
15 Race 250 1 0.4% 0 - 0 - 0 -
16 LastFollowUpDate 250 1 0.4% 0 - 0 - 0 -
17 Age 250 1 0.4% 0 - 0 - 0 -
18 SurgeryDate 250 1 0.4% 0 - 0 - 0 -
19 Name 250 1 0.4% 0 - 0 - 0 -
20 DeathTime 250 0 - 0 - 0 - 0 -
21 ID 250 0 - 0 - 0 - 0 -
qDistinct type anomalous_percent
1 3 Logical 56%
2 3 Logical 50.4%
3 3 Logical 32.8%
4 3 Character 0.4%
5 3 Character 0.4%
6 3 Character 0.4%
7 3 Character 0.4%
8 3 Character 0.4%
9 3 Character 0.4%
10 4 Character 0.4%
11 4 Numeric 0.4%
12 4 Numeric 0.4%
13 4 Character 0.4%
14 5 Character 0.4%
15 7 Character 0.4%
16 13 Timestamp 0.4%
17 50 Numeric 0.4%
18 231 Timestamp 0.4%
19 250 Character 0.4%
20 2 Character -
21 250 Character -
$problem_variables
[1] Variable q qNA pNA
[5] qZero pZero qBlank pBlank
[9] qInf pInf qDistinct type
[13] anomalous_percent problems
<0 rows> (or 0-length row.names)
================================================================================
[1] "Ignoring variable LastFollowUpDate: Unsupported type for visualization."
[1] "Ignoring variable SurgeryDate: Unsupported type for visualization."
Variable p_1 p_10 p_25 p_50 p_75 p_90 p_99
1 Anti-X-intensity 1 2 2 2 3 3 3
2 Anti-Y-intensity 1 1 1 2 3 3 3
3 Age 26 31 37 48 61 68 73
Summary of Data via DataExplorer 📦
# A tibble: 1 x 9
rows columns discrete_columns continuous_colu… all_missing_col…
<int> <int> <int> <int> <int>
1 250 21 18 3 0
# … with 4 more variables: total_missing_values <int>, complete_rows <int>,
# total_observations <int>, memory_usage <dbl>
Drop columns
Write results as described in (Knijn, Simmer, and Nagtegaal 2015)22
Describe the number of patients included in the analysis and reason for dropout
Report patient/disease characteristics (including the biomarker of interest) with the number of missing values
Describe the interaction of the biomarker of interest with established prognostic variables
Include at least 90 % of initial cases included in univariate and multivariate analyses
Report the estimated effect (relative risk/odds ratio, confidence interval, and p value) in univariate analysis
Report the estimated effect (hazard rate/odds ratio, confidence interval, and p value) in multivariate analysis
Report the estimated effects (hazard ratio/odds ratio, confidence interval, and p value) of other prognostic factors included in multivariate analysis
Codes for Descriptive Statistics.23
Report Data properties via report 📦
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others
- Name: 249 entries: Adayah, n = 1; Adeja, n = 1; Adelaina, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Male, n = 135; Female, n = 114 (1 missing)
- Age: Mean = 49.05, SD = 13.68, range = [25, 73], 1 missing
- Race: 6 entries: White, n = 158; Hispanic, n = 46; Black, n = 33 and 3 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 192; Present, n = 57 (1 missing)
- LVI: 2 entries: Absent, n = 163; Present, n = 86 (1 missing)
- PNI: 2 entries: Absent, n = 174; Present, n = 75 (1 missing)
- Death: 2 levels: FALSE (n = 81); TRUE (n = 168) and missing (n = 1)
- Group: 2 entries: Treatment, n = 128; Control, n = 121 (1 missing)
- Grade: 3 entries: 3, n = 101; 1, n = 80; 2, n = 68 (1 missing)
- TStage: 4 entries: 4, n = 102; 3, n = 73; 2, n = 52 and 1 other (1 missing)
- Anti-X-intensity: Mean = 2.41, SD = 0.62, range = [1, 3], 1 missing
- Anti-Y-intensity: Mean = 1.97, SD = 0.77, range = [1, 3], 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 143; Present, n = 106 (1 missing)
- Valid: 2 levels: FALSE (n = 139); TRUE (n = 110) and missing (n = 1)
- Smoker: 2 levels: FALSE (n = 125); TRUE (n = 124) and missing (n = 1)
- Grade_Level: 3 entries: high, n = 96; moderate, n = 79; low, n = 74 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101
Table 1 via arsenal 📦
# cat(names(mydata), sep = ' + \n')
library(arsenal)
tab1 <- arsenal::tableby(~Sex + Age + Race + PreinvasiveComponent + LVI + PNI + Death +
Group + Grade + TStage + `Anti-X-intensity` + `Anti-Y-intensity` + LymphNodeMetastasis +
Valid + Smoker + Grade_Level, data = mydata)
summary(tab1)| Overall (N=250) | |
|---|---|
| Sex | |
| N-Miss | 1 |
| Female | 114 (45.8%) |
| Male | 135 (54.2%) |
| Age | |
| N-Miss | 1 |
| Mean (SD) | 49.048 (13.681) |
| Range | 25.000 - 73.000 |
| Race | |
| N-Miss | 1 |
| Asian | 7 (2.8%) |
| Bi-Racial | 4 (1.6%) |
| Black | 33 (13.3%) |
| Hispanic | 46 (18.5%) |
| Other | 1 (0.4%) |
| White | 158 (63.5%) |
| PreinvasiveComponent | |
| N-Miss | 1 |
| Absent | 192 (77.1%) |
| Present | 57 (22.9%) |
| LVI | |
| N-Miss | 1 |
| Absent | 163 (65.5%) |
| Present | 86 (34.5%) |
| PNI | |
| N-Miss | 1 |
| Absent | 174 (69.9%) |
| Present | 75 (30.1%) |
| Death | |
| N-Miss | 1 |
| FALSE | 81 (32.5%) |
| TRUE | 168 (67.5%) |
| Group | |
| N-Miss | 1 |
| Control | 121 (48.6%) |
| Treatment | 128 (51.4%) |
| Grade | |
| N-Miss | 1 |
| 1 | 80 (32.1%) |
| 2 | 68 (27.3%) |
| 3 | 101 (40.6%) |
| TStage | |
| N-Miss | 1 |
| 1 | 22 (8.8%) |
| 2 | 52 (20.9%) |
| 3 | 73 (29.3%) |
| 4 | 102 (41.0%) |
| Anti-X-intensity | |
| N-Miss | 1 |
| Mean (SD) | 2.406 (0.622) |
| Range | 1.000 - 3.000 |
| Anti-Y-intensity | |
| N-Miss | 1 |
| Mean (SD) | 1.968 (0.772) |
| Range | 1.000 - 3.000 |
| LymphNodeMetastasis | |
| N-Miss | 1 |
| Absent | 143 (57.4%) |
| Present | 106 (42.6%) |
| Valid | |
| N-Miss | 1 |
| FALSE | 139 (55.8%) |
| TRUE | 110 (44.2%) |
| Smoker | |
| N-Miss | 1 |
| FALSE | 125 (50.2%) |
| TRUE | 124 (49.8%) |
| Grade_Level | |
| N-Miss | 1 |
| high | 96 (38.6%) |
| low | 74 (29.7%) |
| moderate | 79 (31.7%) |
Table 1 via tableone 📦
library(tableone)
mydata %>% select(-keycolumns, -dateVariables) %>% tableone::CreateTableOne(data = .)
Overall
n 250
Sex = Male (%) 135 (54.2)
Age (mean (SD)) 49.05 (13.68)
Race (%)
Asian 7 ( 2.8)
Bi-Racial 4 ( 1.6)
Black 33 (13.3)
Hispanic 46 (18.5)
Other 1 ( 0.4)
White 158 (63.5)
PreinvasiveComponent = Present (%) 57 (22.9)
LVI = Present (%) 86 (34.5)
PNI = Present (%) 75 (30.1)
Death = TRUE (%) 168 (67.5)
Group = Treatment (%) 128 (51.4)
Grade (%)
1 80 (32.1)
2 68 (27.3)
3 101 (40.6)
TStage (%)
1 22 ( 8.8)
2 52 (20.9)
3 73 (29.3)
4 102 (41.0)
Anti-X-intensity (mean (SD)) 2.41 (0.62)
Anti-Y-intensity (mean (SD)) 1.97 (0.77)
LymphNodeMetastasis = Present (%) 106 (42.6)
Valid = TRUE (%) 110 (44.2)
Smoker = TRUE (%) 124 (49.8)
Grade_Level (%)
high 96 (38.6)
low 74 (29.7)
moderate 79 (31.7)
DeathTime = Within1Year (%) 149 (59.6)
Descriptive Statistics of Continuous Variables
mydata %>% select(continiousVariables, numericVariables, integerVariables) %>% summarytools::descr(.,
style = "rmarkdown") variable type na na_pct unique min mean max
1 Sex chr 1 0.4 3 NA NA NA
2 PreinvasiveComponent chr 1 0.4 3 NA NA NA
3 LVI chr 1 0.4 3 NA NA NA
4 PNI chr 1 0.4 3 NA NA NA
5 Death lgl 1 0.4 3 0 0.67 1
6 Group chr 1 0.4 3 NA NA NA
7 Grade chr 1 0.4 4 NA NA NA
8 Anti-X-intensity dbl 1 0.4 4 1 2.41 3
9 Anti-Y-intensity dbl 1 0.4 4 1 1.97 3
10 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
11 Valid lgl 1 0.4 3 0 0.44 1
12 Smoker lgl 1 0.4 3 0 0.50 1
13 Grade_Level chr 1 0.4 4 NA NA NA
14 DeathTime chr 0 0.0 2 NA NA NA
variable type na na_pct unique min mean max
1 Name chr 1 0.4 250 NA NA NA
2 Sex chr 1 0.4 3 NA NA NA
3 Age dbl 1 0.4 50 25 49.05 73
4 Race chr 1 0.4 7 NA NA NA
5 PreinvasiveComponent chr 1 0.4 3 NA NA NA
6 LVI chr 1 0.4 3 NA NA NA
7 PNI chr 1 0.4 3 NA NA NA
8 LastFollowUpDate dat 1 0.4 13 NA NA NA
9 Death lgl 1 0.4 3 0 0.67 1
10 Group chr 1 0.4 3 NA NA NA
11 Grade chr 1 0.4 4 NA NA NA
12 TStage chr 1 0.4 5 NA NA NA
13 Anti-X-intensity dbl 1 0.4 4 1 2.41 3
14 Anti-Y-intensity dbl 1 0.4 4 1 1.97 3
15 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
16 Valid lgl 1 0.4 3 0 0.44 1
17 Smoker lgl 1 0.4 3 0 0.50 1
18 Grade_Level chr 1 0.4 4 NA NA NA
19 SurgeryDate dat 1 0.4 231 NA NA NA
variable type na na_pct unique min mean max
1 ID chr 0 0.0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 49.05 73
5 Race chr 1 0.4 7 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 1 0.4 3 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.67 1
11 Group chr 1 0.4 3 NA NA NA
12 Grade chr 1 0.4 4 NA NA NA
13 TStage chr 1 0.4 5 NA NA NA
14 Anti-X-intensity dbl 1 0.4 4 1 2.41 3
15 Anti-Y-intensity dbl 1 0.4 4 1 1.97 3
16 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
17 Valid lgl 1 0.4 3 0 0.44 1
18 Smoker lgl 1 0.4 3 0 0.50 1
19 Grade_Level chr 1 0.4 4 NA NA NA
20 SurgeryDate dat 1 0.4 231 NA NA NA
21 DeathTime chr 0 0.0 2 NA NA NA
Use R/gc_desc_cat.R to generate gc_desc_cat.Rmd containing descriptive statistics for categorical variables
mydata %>% janitor::tabyl(Sex) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Sex | n | percent | valid_percent |
|---|---|---|---|
| Female | 114 | 45.6% | 45.8% |
| Male | 135 | 54.0% | 54.2% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Race) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Race | n | percent | valid_percent |
|---|---|---|---|
| Asian | 7 | 2.8% | 2.8% |
| Bi-Racial | 4 | 1.6% | 1.6% |
| Black | 33 | 13.2% | 13.3% |
| Hispanic | 46 | 18.4% | 18.5% |
| Other | 1 | 0.4% | 0.4% |
| White | 158 | 63.2% | 63.5% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(PreinvasiveComponent) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| PreinvasiveComponent | n | percent | valid_percent |
|---|---|---|---|
| Absent | 192 | 76.8% | 77.1% |
| Present | 57 | 22.8% | 22.9% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(LVI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| LVI | n | percent | valid_percent |
|---|---|---|---|
| Absent | 163 | 65.2% | 65.5% |
| Present | 86 | 34.4% | 34.5% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(PNI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| PNI | n | percent | valid_percent |
|---|---|---|---|
| Absent | 174 | 69.6% | 69.9% |
| Present | 75 | 30.0% | 30.1% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Group) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Group | n | percent | valid_percent |
|---|---|---|---|
| Control | 121 | 48.4% | 48.6% |
| Treatment | 128 | 51.2% | 51.4% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Grade | n | percent | valid_percent |
|---|---|---|---|
| 1 | 80 | 32.0% | 32.1% |
| 2 | 68 | 27.2% | 27.3% |
| 3 | 101 | 40.4% | 40.6% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(TStage) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| TStage | n | percent | valid_percent |
|---|---|---|---|
| 1 | 22 | 8.8% | 8.8% |
| 2 | 52 | 20.8% | 20.9% |
| 3 | 73 | 29.2% | 29.3% |
| 4 | 102 | 40.8% | 41.0% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(LymphNodeMetastasis) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| LymphNodeMetastasis | n | percent | valid_percent |
|---|---|---|---|
| Absent | 143 | 57.2% | 57.4% |
| Present | 106 | 42.4% | 42.6% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade_Level) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Grade_Level | n | percent | valid_percent |
|---|---|---|---|
| high | 96 | 38.4% | 38.6% |
| low | 74 | 29.6% | 29.7% |
| moderate | 79 | 31.6% | 31.7% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(DeathTime) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| DeathTime | n | percent |
|---|---|---|
| MoreThan1Year | 101 | 40.4% |
| Within1Year | 149 | 59.6% |
race_stats <- summarytools::freq(mydata$Race)
print(race_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE, Variable.label = "Race Group")variable = PreinvasiveComponent
type = character
na = 1 of 250 (0.4%)
unique = 3
Absent = 192 (76.8%)
Present = 57 (22.8%)
NA = 1 (0.4%)
## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(mydata, Target = NULL, margin = 1, clim = 10, nlim = 5, round = 2,
bin = NULL, per = T) Variable Valid Frequency Percent CumPercent
1 Sex Female 114 45.6 45.6
2 Sex Male 135 54.0 99.6
3 Sex NA 1 0.4 100.0
4 Sex TOTAL 250 NA NA
5 Race Asian 7 2.8 2.8
6 Race Bi-Racial 4 1.6 4.4
7 Race Black 33 13.2 17.6
8 Race Hispanic 46 18.4 36.0
9 Race NA 1 0.4 36.4
10 Race Other 1 0.4 36.8
11 Race White 158 63.2 100.0
12 Race TOTAL 250 NA NA
13 PreinvasiveComponent Absent 192 76.8 76.8
14 PreinvasiveComponent NA 1 0.4 77.2
15 PreinvasiveComponent Present 57 22.8 100.0
16 PreinvasiveComponent TOTAL 250 NA NA
17 LVI Absent 163 65.2 65.2
18 LVI NA 1 0.4 65.6
19 LVI Present 86 34.4 100.0
20 LVI TOTAL 250 NA NA
21 PNI Absent 174 69.6 69.6
22 PNI NA 1 0.4 70.0
23 PNI Present 75 30.0 100.0
24 PNI TOTAL 250 NA NA
25 Group Control 121 48.4 48.4
26 Group NA 1 0.4 48.8
27 Group Treatment 128 51.2 100.0
28 Group TOTAL 250 NA NA
29 Grade 1 80 32.0 32.0
30 Grade 2 68 27.2 59.2
31 Grade 3 101 40.4 99.6
32 Grade NA 1 0.4 100.0
33 Grade TOTAL 250 NA NA
34 TStage 1 22 8.8 8.8
35 TStage 2 52 20.8 29.6
36 TStage 3 73 29.2 58.8
37 TStage 4 102 40.8 99.6
38 TStage NA 1 0.4 100.0
39 TStage TOTAL 250 NA NA
40 LymphNodeMetastasis Absent 143 57.2 57.2
41 LymphNodeMetastasis NA 1 0.4 57.6
42 LymphNodeMetastasis Present 106 42.4 100.0
43 LymphNodeMetastasis TOTAL 250 NA NA
44 Grade_Level high 96 38.4 38.4
45 Grade_Level low 74 29.6 68.0
46 Grade_Level moderate 79 31.6 99.6
47 Grade_Level NA 1 0.4 100.0
48 Grade_Level TOTAL 250 NA NA
49 DeathTime MoreThan1Year 101 40.4 40.4
50 DeathTime Within1Year 149 59.6 100.0
51 DeathTime TOTAL 250 NA NA
52 Anti-X-intensity 1 18 7.2 7.2
53 Anti-X-intensity 2 112 44.8 52.0
54 Anti-X-intensity 3 119 47.6 99.6
55 Anti-X-intensity NA 1 0.4 100.0
56 Anti-X-intensity TOTAL 250 NA NA
57 Anti-Y-intensity 1 78 31.2 31.2
58 Anti-Y-intensity 2 101 40.4 71.6
59 Anti-Y-intensity 3 70 28.0 99.6
60 Anti-Y-intensity NA 1 0.4 100.0
61 Anti-Y-intensity TOTAL 250 NA NA
# A tibble: 16 x 5
col_name cnt common common_pcnt levels
<chr> <int> <chr> <dbl> <named list>
1 Death 3 TRUE 67.2 <tibble [3 × 3]>
2 DeathTime 2 Within1Year 59.6 <tibble [2 × 3]>
3 Grade 4 3 40.4 <tibble [4 × 3]>
4 Grade_Level 4 high 38.4 <tibble [4 × 3]>
5 Group 3 Treatment 51.2 <tibble [3 × 3]>
6 ID 250 001 0.4 <tibble [250 × 3]>
7 LVI 3 Absent 65.2 <tibble [3 × 3]>
8 LymphNodeMetastasis 3 Absent 57.2 <tibble [3 × 3]>
9 Name 250 Adayah 0.4 <tibble [250 × 3]>
10 PNI 3 Absent 69.6 <tibble [3 × 3]>
11 PreinvasiveComponent 3 Absent 76.8 <tibble [3 × 3]>
12 Race 7 White 63.2 <tibble [7 × 3]>
13 Sex 3 Male 54 <tibble [3 × 3]>
14 Smoker 3 FALSE 50 <tibble [3 × 3]>
15 TStage 5 4 40.8 <tibble [5 × 3]>
16 Valid 3 FALSE 55.6 <tibble [3 × 3]>
# A tibble: 3 x 3
value prop cnt
<chr> <dbl> <int>
1 Treatment 0.512 128
2 Control 0.484 121
3 <NA> 0.004 1
summarytools::stby(list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), mydata$PNI,
summarytools::ctable)SmartEDA::ExpCTable(mydata, Target = "Sex", margin = 1, clim = 10, nlim = NULL, round = 2,
bin = 4, per = F) VARIABLE CATEGORY Sex:Female Sex:Male Sex:NA TOTAL
1 Race Asian 3 3 1 7
2 Race Bi-Racial 2 2 0 4
3 Race Black 9 24 0 33
4 Race Hispanic 24 22 0 46
5 Race NA 0 1 0 1
6 Race Other 0 1 0 1
7 Race White 76 82 0 158
8 Race TOTAL 114 135 1 250
9 PreinvasiveComponent Absent 86 106 0 192
10 PreinvasiveComponent NA 0 0 1 1
11 PreinvasiveComponent Present 28 29 0 57
12 PreinvasiveComponent TOTAL 114 135 1 250
13 LVI Absent 75 87 1 163
14 LVI NA 0 1 0 1
15 LVI Present 39 47 0 86
16 LVI TOTAL 114 135 1 250
17 PNI Absent 77 97 0 174
18 PNI NA 1 0 0 1
19 PNI Present 36 38 1 75
20 PNI TOTAL 114 135 1 250
21 Group Control 60 61 0 121
22 Group NA 1 0 0 1
23 Group Treatment 53 74 1 128
24 Group TOTAL 114 135 1 250
25 Grade 1 38 42 0 80
26 Grade 2 37 30 1 68
27 Grade 3 38 63 0 101
28 Grade NA 1 0 0 1
29 Grade TOTAL 114 135 1 250
30 TStage 1 12 10 0 22
31 TStage 2 23 29 0 52
32 TStage 3 34 39 0 73
33 TStage 4 44 57 1 102
34 TStage NA 1 0 0 1
35 TStage TOTAL 114 135 1 250
36 LymphNodeMetastasis Absent 68 74 1 143
37 LymphNodeMetastasis NA 1 0 0 1
38 LymphNodeMetastasis Present 45 61 0 106
39 LymphNodeMetastasis TOTAL 114 135 1 250
40 Grade_Level high 36 60 0 96
41 Grade_Level low 39 35 0 74
42 Grade_Level moderate 39 39 1 79
43 Grade_Level NA 0 1 0 1
44 Grade_Level TOTAL 114 135 1 250
45 DeathTime MoreThan1Year 51 50 0 101
46 DeathTime Within1Year 63 85 1 149
47 DeathTime TOTAL 114 135 1 250
48 Anti-X-intensity 1 8 10 0 18
49 Anti-X-intensity 2 50 62 0 112
50 Anti-X-intensity 3 56 62 1 119
51 Anti-X-intensity NA 0 1 0 1
52 Anti-X-intensity TOTAL 114 135 1 250
53 Anti-Y-intensity 1 38 39 1 78
54 Anti-Y-intensity 2 51 50 0 101
55 Anti-Y-intensity 3 25 45 0 70
56 Anti-Y-intensity NA 0 1 0 1
57 Anti-Y-intensity TOTAL 114 135 1 250
mydata %>% select(characterVariables) %>% select(PreinvasiveComponent, PNI, LVI) %>%
reactable::reactable(data = ., groupBy = c("PreinvasiveComponent", "PNI"), columns = list(LVI = reactable::colDef(aggregate = "count")))Descriptive Statistics Age
mydata %>% jmv::descriptives(data = ., vars = "Age", hist = TRUE, dens = TRUE, box = TRUE,
violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, skew = TRUE,
kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
─────────────────────────────────
Age
─────────────────────────────────
N 249
Missing 1
Mean 49.0
Median 48.0
Mode 43.0
Standard deviation 13.7
Variance 187
Minimum 25.0
Maximum 73.0
Skewness 0.0330
Std. error skewness 0.154
Kurtosis -1.20
Std. error kurtosis 0.307
25th percentile 37.0
50th percentile 48.0
75th percentile 61.0
─────────────────────────────────
Descriptive Statistics Anti-X-intensity
mydata %>% jmv::descriptives(data = ., vars = "Anti-X-intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
───────────────────────────────────────────
Anti-X-intensity
───────────────────────────────────────────
N 249
Missing 1
Mean 2.41
Median 2.00
Mode 3.00
Standard deviation 0.622
Variance 0.387
Minimum 1.00
Maximum 3.00
Skewness -0.548
Std. error skewness 0.154
Kurtosis -0.608
Std. error kurtosis 0.307
25th percentile 2.00
50th percentile 2.00
75th percentile 3.00
───────────────────────────────────────────
Descriptive Statistics Anti-Y-intensity
mydata %>% jmv::descriptives(data = ., vars = "Anti-Y-intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
───────────────────────────────────────────
Anti-Y-intensity
───────────────────────────────────────────
N 249
Missing 1
Mean 1.97
Median 2.00
Mode 2.00
Standard deviation 0.772
Variance 0.596
Minimum 1.00
Maximum 3.00
Skewness 0.0552
Std. error skewness 0.154
Kurtosis -1.32
Std. error kurtosis 0.307
25th percentile 1.00
50th percentile 2.00
75th percentile 3.00
───────────────────────────────────────────
Overall
n 250
Age (mean (SD)) 49.05 (13.68)
Anti-X-intensity (mean (SD)) 2.41 (0.62)
Anti-Y-intensity (mean (SD)) 1.97 (0.77)
Overall
n 250
Age (mean (SD)) 49.05 (13.68)
Anti-X-intensity (median [IQR]) 2.00 [2.00, 3.00]
Anti-Y-intensity (mean (SD)) 1.97 (0.77)
variable = Age
type = double
na = 1 of 250 (0.4%)
unique = 50
min|max = 25 | 73
q05|q95 = 28 | 70
q25|q75 = 37 | 61
median = 48
mean = 49.04819
mydata %>% select(continiousVariables) %>% SmartEDA::ExpNumStat(data = ., by = "A",
gp = NULL, Qnt = seq(0, 1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)# A tibble: 3 x 10
col_name min q1 median mean q3 max sd pcnt_na hist
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <named list>
1 Age 25 37 48 49.0 61 73 13.7 0.4 <tibble [12…
2 Anti-X-inten… 1 2 2 2.41 3 3 0.622 0.4 <tibble [12…
3 Anti-Y-inten… 1 1 2 1.97 3 3 0.772 0.4 <tibble [12…
# A tibble: 27 x 2
value prop
<chr> <dbl>
1 [-Inf, 24) 0
2 [24, 26) 0.00803
3 [26, 28) 0.0361
4 [28, 30) 0.0281
5 [30, 32) 0.0402
6 [32, 34) 0.0482
7 [34, 36) 0.0562
8 [36, 38) 0.0361
9 [38, 40) 0.0402
10 [40, 42) 0.0482
# … with 17 more rows
summarytools::stby(data = mydata, INDICES = mydata$PreinvasiveComponent, FUN = summarytools::descr,
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)with(mydata, summarytools::stby(Age, PreinvasiveComponent, summarytools::descr),
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)## Summary statistics by – category
SmartEDA::ExpNumStat(mydata, by = "GA", gp = "PreinvasiveComponent", Qnt = seq(0,
1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2) Vname Group TN nNeg nZero nPos NegInf PosInf NA_Value
1 Age PreinvasiveComponent:All 250 0 0 249 0 0 1
2 Age PreinvasiveComponent:Absent 192 0 0 191 0 0 1
3 Age PreinvasiveComponent:Present 57 0 0 57 0 0 0
4 Age PreinvasiveComponent:NA 0 0 0 0 0 0 0
Per_of_Missing sum min max mean median SD CV IQR Skewness Kurtosis
1 0.40 12213 25 73 49.05 48 13.68 0.28 24.0 0.03 -1.20
2 0.52 9143 25 73 47.87 47 13.43 0.28 22.5 0.12 -1.12
3 0.00 3033 27 73 53.21 58 13.86 0.26 24.0 -0.33 -1.24
4 NaN 0 Inf -Inf NaN NA NA NA NA NaN NaN
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% LB.25% UB.75% nOutliers
1 25 31.0 35 40 43 48 54 59 64 68.0 73 1.00 97.00 0
2 25 30.0 34 39 43 47 52 57 62 67.0 73 3.75 93.75 0
3 27 32.6 37 45 49 58 60 63 66 69.4 73 5.00 101.00 0
4 NA NA NA NA NA NA NA NA NA NA NA NA NA 0
Codes for Survival Analysis24
https://link.springer.com/article/10.1007/s00701-019-04096-9
Calculate survival time
mydata$int <- lubridate::interval(lubridate::ymd(mydata$SurgeryDate), lubridate::ymd(mydata$LastFollowUpDate))
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)recode death status outcome as numbers for survival analysis
## Recoding mydata$Death into mydata$Outcome
mydata$Outcome <- forcats::fct_recode(as.character(mydata$Death), `1` = "TRUE", `0` = "FALSE")
mydata$Outcome <- as.numeric(as.character(mydata$Outcome))it is always a good practice to double-check after recoding25
0 1
FALSE 81 0
TRUE 0 168
library(survival)
# data(lung) km <- with(lung, Surv(time, status))
km <- with(mydata, Surv(OverallTime, Outcome))
head(km, 80) [1] 3.4 7.7 6.3 6.6 9.4 9.1 6.5+ 10.5+ 8.2 8.7 11.6 11.3
[13] 4.8 6.7 11.6+ 6.2+ 7.2+ 6.4 3.3 9.7 11.4+ 8.0+ 9.4 10.9+
[25] 5.1+ 8.9 3.5 9.0 3.8+ 5.8 10.2+ 3.7 8.3+ 7.3 3.1 5.9
[37] 8.0+ 7.4 9.5+ 3.7+ 10.2 3.9 9.1+ 6.6 7.3+ 5.5 5.4 3.8
[49] 8.8+ 8.8 3.2 7.0 10.7 6.6 8.6 5.5 11.5 9.7 NA 10.5
[61] 7.5 9.0 7.8 10.6 5.0 6.3 4.3 4.4+ 7.5 6.3 8.5+ 9.0+
[73] 7.1 6.0 5.8+ 3.0 8.2 8.6 5.7 7.2+
Kaplan-Meier Plot Log-Rank Test
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "LVI"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
mydata %>%
finalfit::surv_plot(.data = .,
dependent = "Surv(OverallTime, Outcome)",
explanatory = "LVI",
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)library(finalfit)
library(survival)
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mydata %>% finalfit::finalfit(dependentUni, explanatoryUni)
knitr::kable(tUni, row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))| Dependent: Surv(OverallTime, Outcome) | all | HR (univariable) | HR (multivariable) | |
|---|---|---|---|---|
| LVI | Absent | 163 (100.0) | NA | NA |
| Present | 86 (100.0) | 1.09 (0.77-1.54, p=0.613) | 1.09 (0.77-1.54, p=0.613) |
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names()
tUni_df_descr <- paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1],
" is ", tUni_df$x[2], ", there is ", tUni_df$hr_univariable[2], " times risk than ",
"when ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1],
".")When LVI is Present, there is 1.09 (0.77-1.54, p=0.613) times risk than when LVI is Absent.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 161 119 17.6 13.6 23.5
LVI=Present 85 46 10.6 9.4 26.0
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names() %>%
tibble::rownames_to_column()km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When {rowname}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::select(description) %>% pull()When LVI=Absent, median survival is 17.6 [13.6 - 23.5, 95% CI] months., When LVI=Present, median survival is 10.6 [9.4 - 26, 95% CI] months.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
4 observations deleted due to missingness
LVI=Absent
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 81 61 0.587 0.0409 0.513 0.673
36 18 45 0.211 0.0378 0.148 0.300
LVI=Present
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 15 38 0.409 0.0671 0.297 0.564
36 6 4 0.273 0.0721 0.163 0.458
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% pull()When LVI=Absent, 12 month survival is 58.7% [51.3%-67%, 95% CI]., When LVI=Absent, 36 month survival is 21.1% [14.8%-30%, 95% CI]., When LVI=Present, 12 month survival is 40.9% [29.7%-56%, 95% CI]., When LVI=Present, 36 month survival is 27.3% [16.3%-46%, 95% CI].
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "TStage"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)Interpret the results in context of the working hypothesis elaborated in the introduction and other relevant studies; include a discussion of limitations of the study.
Discuss potential clinical applications and implications for future research
Knijn, N., F. Simmer, and I. D. Nagtegaal. 2015. “Recommendations for Reporting Histopathology Studies: A Proposal.” Virchows Archiv 466 (6): 611–15. https://doi.org/10.1007/s00428-015-1762-3.
Schmidt, Robert L., Deborah J. Chute, Jorie M. Colbert-Getz, Adolfo Firpo-Betancourt, Daniel S. James, Julie K. Karp, Douglas C. Miller, et al. 2017. “Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty.” Archives of Pathology & Laboratory Medicine 141 (2): 279–87. https://doi.org/10.5858/arpa.2016-0200-OA.
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3↩︎
See childRmd/_01header.Rmd file for other general settings↩︎
Change echo = FALSE to hide codes after knitting.↩︎
See childRmd/_02fakeData.Rmd file for other codes↩︎
Synthea The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak 19, 44 (2019) doi:10.1186/s12911-019-0793-0↩︎
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0793-0↩︎
https://medium.com/free-code-camp/how-our-test-data-generator-makes-fake-data-look-real-ace01c5bde4a↩︎
lung, cancer, breast datası ile birleştir↩︎
See childRmd/_03importData.Rmd file for other codes↩︎
See childRmd/_04briefSummary.Rmd file for other codes↩︎
Kişisel verilerin kaydedilmesi ve kişisel verileri hukuka aykırı olarak verme veya ele geçirme Türk Ceza Kanunu’nun 135. ve 136. maddesi kapsamında bizim hukuk sistemimizde suç olarak tanımlanmıştır. Kişisel verilerin kaydedilmesi suçunun cezası 1 ila 3 yıl hapis cezasıdır. Suçun nitelikli hali ise, kamu görevlisi tarafından görevin verdiği yetkinin kötüye kullanılarak veya belirli bir meslek veya sanatın sağladığı kolaylıktan yararlanılarak işlenmesidir ki bu durumda suçun cezası 1.5 ile 4.5 yıl hapis cezası olacaktır.↩︎
See childRmd/_06variableTypes.Rmd file for other codes↩︎
See childRmd/_07overView.Rmd file for other codes↩︎
Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine: February 2017, Vol. 141, No. 2, pp. 279-287. https://doi.org/10.5858/arpa.2016-0200-OA↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3↩︎
See childRmd/_11descriptives.Rmd file for other codes↩︎
See childRmd/_18survival.Rmd file for other codes, and childRmd/_19shinySurvival.Rmd for shiny application↩︎
JAMA retraction after miscoding – new Finalfit function to check recoding↩︎
See childRmd/_23footer.Rmd file for other codes↩︎
Smith AM, Katz DS, Niemeyer KE, FORCE11 Software Citation Working Group. (2016) Software Citation Principles. PeerJ Computer Science 2:e86. DOI: 10.7717/peerj-cs.86 https://www.force11.org/software-citation-principles↩︎
A work by Serdar Balci
drserdarbalci@gmail.com